Magnetic resonance imaging (MRI) is a highly exible and non-invasive medical imaging modality based on the concept of nuclear magnetic resonance (NMR). Compared to other imaging techniques, major limitation of MRI is the relatively long acquisition time. The slowness of acquisition makes MRI difficult to apply to time-sensitive clinical applications. Acquisition of MRA images with a spatial resolution close to conventional digital subtraction angiography is feasible, but at the expense of reduction in temporal resolution. Parallel MRI employs multiple receiver coils to speed up the MRI acquisition by reducing the number of data points collected. Although, the reconstructed images from undersampled data sets often suffer from different different types of degradation and artifacts. In contrast-enhanced magnetic resonance imaging, information is effectively measured in 3D k-space one line at a time therefore the 3D data acquisition extends over several minutes even using parallel receiver coils. This limits the assessment of high ow lesions and some vascular tumors in patients. To improve spatio-temporal resolution in contrast enhanced magnetic resonance angiography (CE-MRA), the use of incorporating prior knowledge in the image recovery process is considered in this thesis. There are five contributions in this thesis. The first contribution is the modification of generalized unaliasing using support and sensitivity encoding (GUISE). GUISE was introduced by this group to explore incorporating prior knowledge of the image to be reconstructed in parallel MRI. In order to provide improved time-resolved MRA image sequences of the blood vessels, the GUISE method requires an accurate segmentation of the relatively noisy 3D data set into vessel and background. The method that was originally used for definition of the effective region of support was primitive and produced a segmented image with much false detection because of the effect of overlying structures and the relatively noisy background in images. We proposed to use the statistical principle as employed for the modified maximum intensity projection (MIP) to achieve better 3D segmentation and optimal visualization of blood vessels. In comparison with the previous region of support (ROS), the new one enables higher accelerations MRA reconstructions due to the decreased volume of the ROS and leads to less computationally expensive reconstruction. In the second contribution we demonstrated the impact of imposing the Karhunen-Loeve transform (KLT) basis for the temporal changes, based on prior expectation of the changes in contrast concentration with time. In contrast with other transformation, KLT of the temporal variation showed a better contrast to noise ratio (CNR) can be achieved. By incorporating a data ordering step with compressed sensing (CS), an improvement in image quality for reconstructing parallel MR images was exhibited in prior estimate based compressed sensing (PECS). However, this method required a prior estimate of the image to be available. A singular value decomposition (SVD) modification of PECS algorithm (SPECS) to explore ways of utilising the data ordering step without requiring a prior estimate was extended as the third contribution. By employing singular value decomposition as the sparsifying transform in the CS algorithm, the recovered image was used to derive the data ordering in PECS. The preliminary results outperformed the PECS results. The fourth contribution is a novel approach for training a dictionary for sparse recovery in CE-MRA. The experimental results demonstrate improved reconstructions on clinical undersampled dynamic images. A new method recently has been developed to exploit the structure of the signal in sparse representation. Group sparse compressed sensing (GSCS) allows the efficient reconstruction of signals whose support is contained in the union of a small number of groups (sets) from a collection of pre-defined disjoint groups. Exploiting CS applications in dynamic MR imaging, a group sparse method was introduced for our contrast-enhanced data set. Instead of incorporating data ordering resulted from prior information, pre-defined sparsity patterns were used in the PECS recovery algorithm, resulting to a suppression of noise in the reconstruction.